Characterizing A Property-Driven Obfuscation Strategy

n recent years, code obfuscation has attracted both researchers and software developers as a useful technique for protecting secret properties of proprietary programs. The idea of code obfuscation is to modify a program, while preserving its functionality, in order to make it more difficult to analyze. Thus, the aim of code obfuscation is to conceal certain properties to an attacker, while revealing its intended behavior. However, a general methodology for deriving an obfuscating transforma- tion from the properties to conceal and reveal is still missing. In this work, we start to address this problem by studying the existence and the characterization of function transformers that minimally or maximally modify a program in order to reveal or conceal a certain property. Based on this general formal framework, we are able to provide a characterization of the maximal obfuscating strategy for transformations concealing a given property while revealing the desired observational behavior. To conclude, we discuss the applicability of the proposed characterization by showing how some common obfuscation techniques can be interpreted in this framework. Moreover, we show how this approach allows us to deeply understand what are the behavioral properties that these transformations conceal, and therefore protect, and which are the ones that they reveal, and therefore disclose

Formal Framework for Property-driven Obfuscations

We study the existence and the characterization of function transformers that minimally or maximally modify a function in order to reveal or conceal a certain property. Based on this general formal framework we develop a strategy for the design of the maximal obfuscating transformation that conceals a given property while revealing the desired observational behaviou

Analyzing program dependences for malware detection.

Metamorphic malware continuously modify their code, while preserving their functionality, in order to foil misuse detection. The key for defeating metamorphism relies in a semantic characterization of the embedding of the malware into the target program. Indeed, a behavioral model of program infection that does not relay on syntactic program features should be able to defeat metamorphism. Moreover, a general model of infection should be able to express dependences and interactions between the malicious codeand the target program. ANI is a general theory for the analysis of dependences of data in a program. We propose an high order theory for ANI, later called HOANI, that allows to study program dependencies. Our idea is then to formalize and study the malware detection problem in terms of HOANI

Inconspicuousness and obfuscation: how large shareholders dynamically manipulate output and information for trading purposes

I model a large shareholder who can affect firm fundamentals. I demonstrate that the large shareholder amplifies the component of his private information that is unforecastable by uninformed traders and thus alters the fundamental value of the firm to facilitate his trading profits: he obfuscates. I then construct a continuous time dynamic version of the model using Fourier transform methods. In the dynamic model, the large shareholder does not just simply amplify the unforecastable part of the fundamental: he also alters its stochastic structure. The model thus marries market microstructure with real resource allocation. There are two consequences: (i) the large shareholder induces the fundamental value of the firm to more closely mimic the noise traders, and (ii) market liquidity is reduced

Characterizing the Power of Moving Target Defense via Cyber Epidemic Dynamics

Moving Target Defense (MTD) can enhance the resilience of cyber systems against attacks. Although there have been many MTD techniques, there is no systematic understanding and {\em quantitative} characterization of the power of MTD. In this paper, we propose to use a cyber epidemic dynamics approach to characterize the power of MTD. We define and investigate two complementary measures that are applicable when the defender aims to deploy MTD to achieve a certain security goal. One measure emphasizes the maximum portion of time during which the system can afford to stay in an undesired configuration (or posture), without considering the cost of deploying MTD. The other measure emphasizes the minimum cost of deploying MTD, while accommodating that the system has to stay in an undesired configuration (or posture) for a given portion of time. Our analytic studies lead to algorithms for optimally deploying MTD.Comment: 12 pages; 4 figures; Hotsos 14, 201

HOL(y)Hammer: Online ATP Service for HOL Light

HOL(y)Hammer is an online AI/ATP service for formal (computer-understandable) mathematics encoded in the HOL Light system. The service allows its users to upload and automatically process an arbitrary formal development (project) based on HOL Light, and to attack arbitrary conjectures that use the concepts defined in some of the uploaded projects. For that, the service uses several automated reasoning systems combined with several premise selection methods trained on all the project proofs. The projects that are readily available on the server for such query answering include the recent versions of the Flyspeck, Multivariate Analysis and Complex Analysis libraries. The service runs on a 48-CPU server, currently employing in parallel for each task 7 AI/ATP combinations and 4 decision procedures that contribute to its overall performance. The system is also available for local installation by interested users, who can customize it for their own proof development. An Emacs interface allowing parallel asynchronous queries to the service is also provided. The overall structure of the service is outlined, problems that arise and their solutions are discussed, and an initial account of using the system is given